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Found 7 papers, 0 papers with code
SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation
We present a new methodology for real-time semantic mapping from RGB-D sequences that combines a 2D neural network and a 3D network based on a SLAM system with 3D occupancy mapping.
Self-Supervised Depth Completion for Active Stereo
Active stereo systems are used in many robotic applications that require 3D information.
Towards Bounding-Box Free Panoptic Segmentation
Our approach is distinct from previous works in panoptic segmentation that rely on a combination of a semantic segmentation network with a computationally costly instance segmentation network based on bounding box proposals, such as Mask R-CNN, to guide the prediction of instance labels using a Mixture-of-Expert (MoE) approach.
Street-view change detection with deconvolutional networks
We propose a system for performing structural change detection in street-view videos captured by a vehicle-mounted monocular camera over time.
A Continuous Optimization Approach for Efficient and Accurate Scene Flow
We propose a continuous optimization method for solving dense 3D scene flow problems from stereo imagery.
Noise Models in Feature-based Stereo Visual Odometry
Feature-based visual structure and motion reconstruction pipelines, common in visual odometry and large-scale reconstruction from photos, use the location of corresponding features in different images to determine the 3D structure of the scene, as well as the camera parameters associated with each image.
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs).
